Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds behavioral traits beyond annotations: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list it if it times out. Annotations already declare readOnly, openWorld, idempotent, and non-destructive, so the description enriches context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the purpose and is well-structured. It is slightly verbose but each sentence adds value (e.g., timeouts, fallback). Could trim minor redundancy, but overall effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description explains the return structure: summary block, per-advisory detail, links, and alternative versions. It covers edge cases (partial failures, ecosystem limitation, timing). For a tool with only 2 required params, this is highly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaning: 'Scoped packages (e.g. "@types/node") are accepted' for package param, and 'Defaults to the latest published version when omitted' for version param. This clarifies usage beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs a composite check for npm packages, combining deps.dev and bundlephobia. It specifies the verb 'scan', resource 'dependency', and the exact ecosystem. This distinguishes it from sibling tools like 'validate_claim' or 'deep_research'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' Also provides exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This clearly tells when to use and when to use alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.